Abstract
Background
With rising incidence of skin cancer and relatively increased mortality rates, an improved diagnosis of such a potentially fatal disease is of vital importance. Although frequently curable, it nevertheless places a considerable burden upon healthcare systems. Among the various types of skin cancers, non-melanoma skin cancer is most prevalent. Despite such prevalence and its associated cost, scant proof concerning the diagnostic accuracy via Artificial Intelligence (AI) for non-melanoma skin cancer exists. This study meta-analyzes the diagnostic test accuracy of AI used to diagnose non-melanoma forms of skin cancer, and it identifies potential covariates that account for heterogeneity between extant studies.
Methods
Various electronic databases (Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions) were examined to discern eligible studies beginning from March 2022. Those AI studies predictive of non-melanoma skin cancer were included. Summary estimates of sensitivity, specificity, and area under receiver operating characteristic curves were used to evaluate diagnostic accuracy. The revised Quality Assessment of Diagnostic Studies served to assess any risk of bias.
Results
A literature search produced 39 eligible articles for meta-analysis. The summary sensitivity, specificity, and area under receiver operating characteristic curve of AI for diagnosing non-melanoma skin cancer was 0.78, 0.98, & 0.97, respectively. Skin cancer typology, data sources, cross validation, ensemble models, types of techniques, pre-trained models, and image augmentation became significant covariates accounting for heterogeneity in terms of both sensitivity and/or specificity.
Conclusions
Meta-analysis results revealed that AI is predictive of non-melanoma with an acceptable performance, but sensitivity may become improved. Further, ensemble models and pre-trained models are employable to improve true positive rating.
Similar content being viewed by others
Background
Skin cancer accounts for 32.5% of all diagnosed malignancies, and it has a prevalence of 7.96 million cases occurring globally each year among the general population [1]. With respect to etiology, previous studies have demonstrated a deleterious association with chronic exposure to sunlight because the ultraviolet component induces deoxyribonucleic acid damage which later triggers malignant mutations to occur. Other possible contributors to skin cancer incidence may also include viral infection, drug usage and exposure to chemicals [2].
Pathologically, skin cancer is categorized into either melanoma or non-melanoma. Albeit relatively rare, three hundred thousand annual cases of melanoma are determined as highly malignant, with a reported mortality rate of 1.6 per 100,000 worldwide [1]. By contrast, non-melanoma cases, which comprise a number of pathologically-distinct entities such as basal cell carcinoma and intra-epithelial carcinoma (i.e., actinic keratosis and Bowen’s disease) [3], are less malignant considering Mohs micrographic surgery and a 5-year cure rate of 98.9% [4]. Sixty-five thousand victims die on average, per annum, worldwide due to non-melanoma incidence when combined with a delayed diagnosis factor [1]. Furthermore, non-melanoma skin cancers such as basal cell carcinoma show a trend of increasing cases [5] and are easily misdiagnosed [6]. The abovementioned evidence clearly shows the diagnosis of non-melanoma skin cancer is of similar importance to melanoma forms of skin cancer.
Currently, clinical examination and dermoscopic evaluation are major techniques for screening skin cancers [7]. These screening techniques are estimated to achieve 75–84% of diagnosis accuracy, indicating human error may remain accountable via these approaches [8, 9]. When taking into account the high prevalence and life-threatening risk of this disease, it is important to make a timely diagnosis for appropriate treatment to follow.
Artificial intelligence (AI) techniques are being employed to provide diagnostic assistance to dermatologists since most diagnoses rely principally on visual patterning recognition [10], a particular strength of such a technology. Machine learning is a sub-field of AI which refers to an effort to automate intellectual tasks normally performed by humans; and, deep learning is in turn a subset within machine learning [11]. A veritable plethora of attempts to utilize machine learning techniques aimed at supporting the accurate diagnosis of melanoma and non-melanoma types of skin cancer have already taken place [9, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34]. As such, a systematic reporting is deemed necessary for reliable interpretation and aggregation of these results. However, the comparison of pre-existing skin lesion classification evidence is difficult because differences may exist in the data types used or in the statistical quantities presented [35].
Until present time, synthetic evidence regarding the performance of AI techniques applied for the diagnosis of non-melanoma skin cancer remains insufficient [7, 10]. Without reliable evidence, the application of AI in the diagnosis of non-melanoma skin cancer is frequently obstructed. Furthermore, what important factors/strategies that may influence the performance of AI in the diagnosis of non-melanoma skin cancer are at times unclear.
In viewing the unfulfilled areas of knowledge, the purposes of this meta-analysis are therefore: 1) to meta-analyze the accuracy of diagnosis for non-melanoma skin cancer via machine learning and deep learning; and, 2) to examine potential covariates that can account for the heterogeneity found among these studies. The main contributions of this study are:
-
Summary of the performance of AI for diagnosing non-melanoma skin cancer with quantitative evidence so that AI’s utility assessment can be made with greater efficacy and objectivity.
-
Identification of potential covariates as they relate to AI performance since it may improve through an adoption of those strategies indicated by these identified covariates whenever building AI models.
-
Accumulation of knowledge of diagnostic test accuracy for AI in non-melanoma skin cancer takes place so that earlier and more accurate diagnosis of non-melanoma skin cancer is practical.
The remainder of this paper is structured as follows. Related work section introduces prior reviews on the topic of diagnostic test accuracy, focusing on how these reviews were planned and evaluated. Material and methods section presents the research method adopted in this study. Results section describes the analytical findings based on collected data, Discussion section interprets and describes the significance of the findings, and Conclusions section summarizes the findings of the current study.
Related work
Up until the most recent examples, a number of studies have started to review existing evidence related to AI techniques for skin-lesion classification [7, 10, 23, 35,36,37]. Several themes may be observed from Table 1. First, much evidence is qualitative in nature [10, 35,36,37], except for the study of Sharma et al. [7] and Rajpara et al. [23]. Without quantitative evidence, the performance of AI-based predictive models are not easily or objectively assessed. Second, few reviews [7, 10] have focused solely on non-melanoma forms of skin cancer, with such efforts being devoted to the review of evidence concerning melanoma [16, 23] or both [35, 37]. By focusing exclusively on non-melanoma skin cancer, a better understanding may yet be achieved. Third, most reviews include studies that have adopted machine learning and deep learning, with the exception of Brinker et al. [35]. Despite deep learning being widely considered as having better performance than machine learning, studies that adopted machine learning should also be included in order to have a more holistic understanding of AI performance in the diagnosis of melanoma and non-melanoma skin cancers. Finally, review components/metrics for assessing the performance of AI techniques are quite diversified. Classification methods, data source, and diagnostic accuracy are primary components of these reviews. Further, reviews that followed the Preferred Reporting Items for a Systematic Review and Meta-analysis statement (PRISMA) for Diagnostic Test Accuracy (DTA) commonly reported pooled diagnostic odds ratio, pooled positive/negative likelihood ratio, pooled sensitivity, and pooled specificity, while other reviews usually reported separate accuracy, area under receiver characteristic curve, F1-score, precision, sensitivity, or specificity by individual study. This study therefore follows PRISMA-DTA for reporting summary metrics of included studies for global assessment of AI performance for the diagnosis of non-melanoma skin cancer.
Methods
This study was conducted according to the PRISMA statement [38] (see Additional file 1: Appendix A for diagnostic test accuracy checklist and Additional file 2: Appendix B for diagnostic test accuracy abstracts checklist). The Institutional Review Board of E-Da Hospital (EMRP-108–128) approved the study-wide protocol.
Search strategy and selection process
A literature search, carried out 31st March, 2022, of Scopus, PubMed, ScienceDirect, SpringerLink, and Dimensions, by means of keyword combinations of the terms "basal cell carcinoma", "intra-epithelial carcinoma", "Bowen’s disease", "actinic keratosis", "skin lesion", "non-melanoma skin cancer", "artificial intelligence", "machine learning", and "deep learning".
Inclusion criteria was determined by: 1) studies investigating the accuracy of non-melanoma skin cancer; 2) studies written in English; and, 3) studies adopting machine-learning or deep-learning techniques. Studies were dis-qualified for inclusion, if: 1) they only investigated the incidence of melanoma skin cancer; 2) studies were irrelevant to our research purpose; and, 3) full texts were unavailable for purposes of examination. We located 134 potentially eligible articles, of which 95 were excluded with reason (see Fig. 1), and the remaining 39 articles being included in the eventual quantitative meta-analysis that was made.
Data extraction
From each study, we extracted the following information: Authorship, publication year, sample size, types of non-melanoma skin cancer described, whether data sources were publicly available, whether cross-validation procedures were undertaken, whether ensemble models were employed, and what type of artificial intelligence technique was employed (i.e., deep learning or machine learning). Only studies that adopted a neural network algorithm with more than one hidden layer were categorized as being part of the deep learning group, with others categorized as being part of the machine learning group for purposes of our study. For models based on deep learning, further recorded information including whether pre-trained models were utilized and whether image augmentation was implemented. Further, we extracted the original numbers of true/false positives and true/false negatives from each study to derive outcome measures, including summary sensitivity, specificity, and area under receiver operating characteristic curve, for purposes of diagnostic accuracy. Finally, if an article had classified more than one non-melanoma skin cancer simultaneously, we considered each of the non-melanoma skin cancers as a different study, with relevant data extracted based upon the above-listed procedures.
Methodological analysis
Regarding the quality of each of the included studies, we evaluated the risk of bias and applicability in accordance with the revised Quality Assessment of Diagnostic Studies (QUADAS-2) including four domains: sample selection, index test, reference standard, flow, and timing [30].
Statistical analysis
Following the suggestion of prior evidence [39], sensitivity and specificity were pooled with a bivariate model. Area under receiver operating characteristic curve, diagnostic odds ratio, positive likelihood ratio, and negative likelihood ratio were also estimated in this study. Forest plots were produced to depict variability amongst the studies up for consideration. Besides, summary receiver operating characteristic curves with 95% confidence intervals (CI) and 95% prediction intervals (PI) were adopted to assess the existence of a threshold effect among the included studies [40]. The R statistics [41] with lme4 [42] and mada [43] packages were used for diagnostic accuracy test meta-analysis.
Several meta-regressions with plausible covariates, including types of non-melanoma skin cancer (i.e., basal cell carcinoma and intra-epithelial carcinoma), whether data sources were publicly available (public or proprietary), whether cross-validation procedures were undertaken, whether ensemble models were adopted, types of AI technique employed (machine learning or deep learning), whether pre-trained deep learning models (e.g., DenseNet, ResNet, or AlexNet) were used (Yes or No), and whether image augmentation procedures were used by deep learning models (Yes or No) were undertaken to check for possible heterogeneity among studies. The significance level is set to 0.05 for present study.
Results
General study characteristics
Among the 39 included articles, 13 articles [38]. Common metrics for diagnostic test accuracy including area under receiver operating characteristic curve, sensitivity, specificity, diagnostic odds ratio, positive likelihood ratio and negative likelihood ratio were included. Furthermore, to account for the threshold effect, the pooled sensitivity and specificity was estimated based on a bivariate model [39]. Other metrics such as mean accuracy were not assessed in this study since prior evidence suggests that sensitivity and specificity are more sensible parameters to be analyzed in a meta-analysis, and they are clinically well known [80].
Just like most meta-analyses, our study has its limitations. First, the interpretation of summary sensitivity and specificity should be approached cautiously since heterogeneity among studies exists. Further, 72 studies were excluded due to insufficient quantitative information. Future diagnostic studies aimed at predicting non-melanoma skin cancers are suggested to include sufficient quantitative information for subsequent meta-analysis to better characterize and profile these studies. The covariates identified in this study are purely based from a statistical viewpoint [81], future research could consider the different design ideas of deep learning-based approaches or machine learning-based approaches to identify the incidence of other potential covariates. Finally, future meta-analysis may adopt emerging techniques [82,83,84,85] to cluster or classify models into different groups or categories, so that different insights are obtainable.
Conclusions
Our study aims to meta-analyze the diagnostic test accuracy of applying AI techniques to the diagnosis of non-melanoma type skin cancer which is already considered insufficient in review evidence. Without a better understanding of the performance of AI for the diagnosis of non-melanoma skin cancer, the potential of AI may not be fully realized. Furthermore, the results of this quantitative meta-analysis can provide a more objective synthesis of the AI performance for diagnosing non-melanoma skin cancer. Based on the findings of this study, the usefulness of AI can be assessed with greater facility and objectivity. Moreover, strategies for improving the performance of AI used for screening non-melanoma skin cancer are identifiable. A quick, safe, and non-invasive screening of non-melanoma skin cancers can thus be expected. By searching multiple online databases, 39 articles (67 studies) were included for purposes of meta-analysis. A bivariate meta-analysis of diagnostic test accuracy was undertaken to obtain summary sensitivity, specificity, and AUC. A moderate diagnostic performance of summary sensitivity, a strong summary specificity, and a strong AUC were all observed based according to a bivariate meta-analysis of diagnostic accuracy test. Types of non-melanoma skin cancer, whether data sources were publicly available, whether cross-validation procedures were undertaken, whether ensemble models were adopted, the types of AI technique employed, whether pre-trained deep-learning models were used, and whether image-augmentation procedures were all determined to partially explain some of the heterogeneity found among primary studies. Future studies may consider adopting the suggested techniques to have better predictive performance of AI for the effective diagnosis of non-melanoma skin cancer.
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- AI:
-
Artificial intelligence
- AUC:
-
Area under receiver operating characteristic curve
- BCC:
-
Basal cell carcinoma
- CI:
-
Confidence interval
- CNN:
-
Convolutional neural network
- DOR:
-
Diagnostic odds ratio
- DL:
-
Deep learning
- DTA:
-
Diagnostic test accuracy
- IEC:
-
Intra-epithelial carcinoma
- ± LR:
-
Positive/negative likelihood ratio
- ML:
-
Machine learning
- ROC:
-
Receiver operating characteristic curve
- PRISMA:
-
Preferred reporting items for a systematic review and meta-analysis statement
- PI:
-
Prediction interval
- QUADAS-2:
-
Revised quality assessment of diagnostic studies
References
Global Burden of Disease Cancer Collaboration. Global, Regional, and National Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life-Years for 29 Cancer Groups, 1990 to 2017: A Systematic Analysis for the Global Burden of Disease Study. Jama Oncol. 2019;5(12):1749–68.
Koh HK, Geller AC, Miller DR, Grossbart TA, Lew RA. Prevention and Early Detection Strategies for Melanoma and Skin Cancer: Current Status. Arch Derm. 1996;132(4):436–43.
Tschandl P, Codella N, Akay BN, Argenziano G, Braun RP, Cabo H, Gutman D, Halpern A, Helba B, Hofmann-Wellenhof R, et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019;20(7):938–47.
Madan V, Lear JT, Szeimies R-M. Non-melanoma skin cancer. Lancet. 2010;375(9715):673–85.
Rubin AI, Chen EH, Ratner D. Basal-Cell Carcinoma. N Engl J Med. 2005;353(21):2262–9.
Zhou H, **e F, Jiang Z, Liu J, Wang S, Zhu C. Multi-classification of skin diseases for dermoscopy images using deep learning 2017. Bei**g: IEEE International Conference on Imaging Systems and Techniques (IST); 2017. https://doi.org/10.1109/IST.2017.8261543.
Sharma AN, Shwe S, Mesinkovska NA. Current state of machine learning for non-melanoma skin cancer. Arch Dermatol Res. 2022;314:325–7.
Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Sera F, Binder M, Cerroni L, De Rosa G, Ferrara G, et al. Dermoscopy of pigmented skin lesions: Results of a consensus meeting via the Internet. J Am Acad Dermatol. 2003;48(5):679–93.
Wahba MA, Ashour AS, Guo Y, Napoleon SA, Elnaby MMA. A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification. Comput Meth Prog Bio. 2018;165:163–74.
Marka A, Carter JB, Toto E, Hassanpour S. Automated detection of nonmelanoma skin cancer using digital images: a systematic review. BMC Med Imaging. 2019;19(1):21–21.
Chollet F. Deep Learning with Python (1st ed.). Manning Publications Co. 2018.
Abbas Q. Computer-aided decision support system for classification of pigmented skin lesions. Int J Comput Sci Net Sec. 2016;16(4):9–15.
Ballerini L, Fisher RB, Aldridge B, Rees J. Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. Barcelona: 2012 9th IEEE International Symposium on Biomedical Imaging; 2012. https://doi.org/10.1109/ISBI.2012.6235558.
Cheng B, Stanley RJ, Stoecker WV, Hinton K. Automatic telangiectasia analysis in dermoscopy images using adaptive critic design. Skin Res Technol. 2012;18(4):389–96.
Chuang S-H, Sun X, Chang W-Y, Chen G-S, Huang A, Li J, McKenzie FD. BCC skin cancer diagnosis based on texture analysis techniques. Lake Buena Vista (Orlando): Medical Imaging 2011: Computer-Aided Diagnosis; 2011. https://doi.org/10.1117/12.878124.
Dick V, Sinz C, Mittlböck M, Kittler H, Tschandl P. Accuracy of Computer-Aided Diagnosis of Melanoma: A Meta-analysis. Jama Dermatol. 2019;155(11):1291–9.
Ferris LK, Harkes JA, Gilbert B, Winger DG, Golubets K, Akilov O, Satyanarayanan M. Computer-aided classification of melanocytic lesions using dermoscopic images. J Am Acad Dermatol. 2015;73(5):769–76.
Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. J Invest Dermatol. 2018;138(7):1529–38.
Kharazmi P, Kalia S, Lui H, Wang ZJ, Lee T. Computer-aided detection of basal cell carcinoma through blood content analysis in dermoscopy images Medical Imaging 2019. San Diego: Computer-Aided Diagnosis; 2018b. https://doi.org/10.1117/12.2293353.
Kharazmi P, Kalia S, Lui H, Wang ZJ, Lee TK. A feature fusion system for basal cell carcinoma detection through data-driven feature learning and patient profile. Skin Res Technol. 2018;24(2):256–64.
Kharazmi P, Lui H, Wang ZJ, Lee TK. Automatic detection of basal cell carcinoma using vascular-extracted features from dermoscopy images. Vancouver: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE); 2016. https://doi.org/10.1109/CCECE.2016.7726666.
Olsen TG, Jackson BH, Feeser TA, Kent MN, Moad JC, Krishnamurthy S, Lunsford DD, Soans RE. Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology. J Pathol Inform. 2018;9:32–32.
Rajpara SM, Botello AP, Townend J, Ormerod AD. Systematic review of dermoscopy and digital dermoscopy/ artificial intelligence for the diagnosis of melanoma. Br J Dermatol. 2009;161(3):591–604.
Shimizu K, Iyatomi H, Celebi ME, Norton K-A, Tanaka M. Four-class classification of skin lesions with task decomposition strategy. IEEE T Bio-Med Eng. 2015;62(1):274–83.
Shoieb DA, Youssef SM, Aly WM. Computer-aided model for skin diagnosis using deep learning. J Image Graphics. 2016;4(2):122–9.
Spyridonos P, Gaitanis G, Likas A, Bassukas ID. Automatic discrimination of actinic keratoses from clinical photographs. Comput Biol Med. 2017;88:50–9.
Sriwong K, Bunrit S, Kerdprasop K, Kerdprasop N. Dermatological Classification Using Deep Learning of Skin Image and Patient Background Knowledge. Int J Mach Learn Comput. 2019;9(6):862–7.
Upadhyay PK, Chandra S. Construction of adaptive pulse coupled neural network for abnormality detection in medical images. Appl Artif Intell. 2018;32(5):477–95.
Wahba MA, Ashour AS, Napoleon SA, Abd Elnaby MM, Guo Y. Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine. Health Info Sci Syst. 2017;5(1):10.
Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MMG, Sterne JAC, Bossuyt PMM. the Q-G: QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med. 2011;155(8):529–36.
Yap J, Yolland W, Tschandl P. Multimodal skin lesion classification using deep learning. Exp Dermatol. 2018;27(11):1261–7.
Zhang X, Wang S, Liu J, Tao C. Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med Inform Decis. 2018;18(Suppl 2):59–59.
Nindl I, Gottschling M, Stockfleth E. Human Papillomaviruses and Non-Melanoma Skin Cancer: Basic Virology and Clinical Manifestations. Dis Markers. 2007;23:942650.
Srinivasu PN, SivaSai JG, Ijaz MF, Bhoi AK, Kim W, Kang JJ. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 2021;21(8). https://doi.org/10.3390/s21082852.
Brinker TJ, Hekler A, Utikal JS, Grabe N, Schadendorf D, Klode J, Berking C, Steeb T, Enk AH, von Kalle C. Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review. J Med Internet Res. 2018;20(10):e11936.
Kassem MA, Hosny KM, Damaševičius R, Eltoukhy MM. Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review. Diagnostics. 2021;11(8):1390.
Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for Skin Cancer Detection: Sco** Review. J Med Internet Res. 2021;23(11):e22934.
McInnes MDF, Moher D, Thombs BD, McGrath TA, Bossuyt PM, and the P-DTAG: Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 2018, 319(4):388–396.
Takwoingi Y, Riley RD, Deeks JJ. Meta-analysis of diagnostic accuracy studies in mental health. Evidence Based Mental Health. 2015;18(4):103.
Gatsonis C, Paliwal P. Meta-Analysis of Diagnostic and Screening Test Accuracy Evaluations: Methodologic Primer. Am J Roentgenol. 2006;187(2):271–81.
R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2022. https://www.R-project.org/.
Bates D, Mächler M, Bolker B, Walker S. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw. 2015;67(1). https://doi.org/10.18637/jss.v067.i01.
Doebler P. mada: Meta-Analysis of Diagnostic Accuracy. 2019. https://CRAN.R-project.org/package=mada.
Abhishek K, Kawahara J, Hamarneh G. Predicting the clinical management of skin lesions using deep learning. Sci Rep. 2021;11(1):7769.
Chin CK, Mat DAbA, Saleh AY. Hybrid of convolutional neural network algorithm and autoregressive integrated moving average model for skin cancer classification among Malaysian. IAES International J Artificial Intelligence (IJ-AI). 2021;10(3):707–16.
Chung HJ, Kim YJ, Song H, Ahn SK, Kim H, Hwang H. Deep Learning-Based Classification of Korean Basal Cell Carcinoma Using Convolutional Neural Network. Journal of Medical Imaging and Health Informatics. 2019;9(1):195–201.
Huang K, He X, ** Z, Wu L, Zhao X, Wu Z, . . . Chen X. Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network. J Healthc Eng. 2020:Article 1713904. https://doi.org/10.1155/2020/1713904.
Marvdashti T, Duan L, Aasi SZ, Tang JY, Bowden AKE. Classification of basal cell carcinoma in human skin using machine learning and quantitative features captured by polarization sensitive optical coherence tomography. Biomed Opt Express. 2016;7(9):3721–35.
Rojas JAÁ, Calderón Vilca HD, Tumi Figueroa EN, Ramos KJC, Matos Manguinuri SS, Calderón Vilca EF. Hybrid model of convolutional neural network and support vector machine to classify basal cell carcinoma. Computacion y Sistemas. 2021;25(1):83–95.
Abunadi I, Senan EM. Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases. Electronics. 2021;10(24):3158.
Afza F, Sharif M, Khan MA, Tariq U, Yong H-S, Cha J. Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine. Sensors. 2022;22(3):799.
Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer. Neuroscience Informatics. 2022;2(4):100034.
Al-masni MA, Kim D-H, Kim T-S. Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Programs Biomed. 2020;190:105351.
Bhardwaj S, Somani A, Gupta K. Detection of Skin Lesion Disease Using Deep Learning Algorithm. Delhi: 3rd International Conference on Artificial Intelligence and Speech Technology; 2022. https://doi.org/10.1007/978-3-030-95711-7_32.
Calderón C, Sanchez K, Castillo S, Arguello H. BILSK: A bilinear convolutional neural network approach for skin lesion classification. Comput Methods Programs Biomed Update. 2021;1:100036.
Chaahat G. NK, Lehana PK: An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models. J Healthc Eng. 2021;2021:8113403.
Dorj UO, Lee KK, Choi JY, Lee M. The skin cancer classification using deep convolutional neural network. Multimed Tools Appl. 2018;77(8):9909–24.
Jasil SPG, Ulagamuthalvi V. Deep learning architecture using transfer learning for classification of skin lesions. J Ambient Intell Humanized Comput. 2021. https://doi.org/10.1007/s12652-021-03062-7.
Jørgensen TM, Tycho A, Mogensen M, Bjerring P, Jemec GBE. Machine-learning classification of non-melanoma skin cancers from image features obtained by optical coherence tomography. Skin Res Technol. 2008;14(3):364–9.
Kwiatkowska D, Kluska P, Reich A. Convolutional neural networks for the detection of malignant melanoma in dermoscopy images. Postepy Dermatol Alergol. 2021;38(3):412–20.
Liu J, Wang W, Chen J, Sun G, Yang A. Classification and Research of Skin Lesions Based on Machine Learning. Comput Mater Cont. 2020;62(3):1187–200.
Mobiny A, Singh A, Van Nguyen H. Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis. J Clin Med. 2019;8(8):1241.
Monika MK, Vignesh NA, Kumari CU. Kumar MNVSS, Lydia EL: Skin cancer detection and classification using machine learning. Mater Today Proc. 2020;33:4266–70.
Molina-Molina EO, Solorza-Calderón S, Álvarez-Borrego J. Classification of Dermoscopy Skin Lesion Color-Images Using Fractal-Deep Learning Features. Appl Sci. 2020;10(17):5954.
Swetha RN, Shrivastava VK, Parvathi K. Multiclass skin lesion classification using image augmentation technique and transfer learning models. Int J Intell Unmanned Syst. 2021. https://doi.org/10.1108/IJIUS-02-2021-0010. ahead-of-print(ahead-of-print).
Pratiwi RA, Nurmaini S, Rini DP, Rachmatullah MN, Darmawahyuni A. Deep ensemble learning for skin lesions classification with convolutional neural network. IAES International J Artificial Intelligence (IJ-AI). 2021;10(3):563–70.
Qin Z, Liu Z, Zhu P, Xue Y. A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed. 2020;195:105568.
Rahman Z, Hossain S, Islam R, Hasan M, Hridhee RA. An approach for multiclass skin lesion classification based on ensemble learning. Inform Med Unlocked. 2021;25:100659.
Rizwan W, Adnan SM, Ahmed W, Faizi MI. Skin Lesions Detection and Classification Using Deep Learning. International J Advanced Trends Comput Sci Engineering. 2021;10(3):1720–8.
Sevli O. A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation. Neural Comput Appl. 2021;33(18):12039–50.
Villa-Pulgarin JP, Ruales-Torres AA, Arias-Garzón D, Bravo-Ortiz MA, Arteaga-Arteaga HB, Mora-Rubio A, Alzate-Grisales JA, Mercado-Ruiz E, Hassaballah M, Orozco-Arias S, et al. Optimized convolutional neural network models for skin lesion classification. Comput Mater Continua. 2022;70(2):2131–48.
Yadav U, Kumar A, A T, Mukherjee S. Deep learning in Dermatology for Skin Diseases Detection. International J Recent Technol Eng. 2020;8(6):3929–33.
Provost F, Fawcett T. Data Science for Business: What you need to know about data mining and data-analytic thinking. 2nd ed. Sebastopol, CA: O’Reilly Media, Inc.; 2013.
Kuhn M, Johnson K. Applied Predictive Modeling, vol. New. York. New York: Springer; 2013.
Brownlee J. Ensemble Learning Algorithms with Python. Machine Learning Matery. 2020. https://machinelearningmastery.com/ensemble-learning-algorithms-with-python/.
You K, Liu Y, Zhang Z, Wang J, Jordan MI, Long M. Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs. J Mach Learn Res. 2022;23:1–47.
Han X, Zhang Z, Ding N, Gu Y, Liu X, Huo Y, Qiu J, Yao Y, Zhang A, Zhang L, et al. Pre-trained models: Past, present and future. AI Open. 2021;2:225–50.
Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol. 2021;65(5):545–63.
Khalifa NE, Loey M, Mirjalili S. A comprehensive survey of recent trends in deep learning for digital images augmentation. Artif Intell Rev. 2022;55(3):2351–77.
Zwinderman AH, Bossuyt PM. We should not pool diagnostic likelihood ratios in systematic reviews. Stat Med. 2008;27(5):687–97.
Hareer M, Cuijpers P, Furukawa TA, Ebert DD. Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL: CRC Press; 2021.
Hu L, Pan X, Tang Z, Luo X. A Fast Fuzzy Clustering Algorithm for Complex Networks via a Generalized Momentum Method. IEEE Trans Fuzzy Syst. 2022;30(9):3473–85.
Hu L, Wang X, Huang Y-A, Hu P, You Z-H. A survey on computational models for predicting protein–protein interactions. Brief Bioinform. 2021;22(5):bbab036.
Hu L, Zhang J, Pan X, Yan H, You Z-H. HiSCF: leveraging higher-order structures for clustering analysis in biological networks. Bioinformatics. 2021;37(4):542–50.
Zhao B-W, Hu L, You Z-H, Wang L, Su X-R. HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks. Brief Bioinform. 2022;23(1):bbab515.
Acknowledgements
We want to show gratitude to Mr. Yao-Kun Cheng for hel** data application and collection.
Funding
This study has been supported by E-Da Hospital, Kaohsiung, Taiwan under grant number EDAHP110022 and ISU-108-IUC-04. The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Author information
Authors and Affiliations
Contributions
KMK and CSC conceived of this study and participated in the design and administration of the study. KMK and CSC drafted the manuscript and performed the statistical analysis. PCT reviewed and substantively revised the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. The Institutional Review Board of E-Da Hospital waived their mandate for informed consent regarding this study (IRB No. EMRP-108–128).
Consent for publication
The manuscript does not contain any individual’s data in any form.
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Kuo, K.M., Talley, P.C. & Chang, CS. The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis. BMC Med Inform Decis Mak 23, 138 (2023). https://doi.org/10.1186/s12911-023-02229-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12911-023-02229-w